Today's hydrocarbon production stems to a large part from two types of reservoirs. One type is predominantly composed of siliciclastic rocks or sediments. The other reservoirs are classified as carbonate reservoirs. As the latter reservoir type is at the focus of the present invention, it is worth noting that the interpretation of log data derived from well measurement and the accuracy of the interpretation differ significantly depending on the type of reservoir. These differences emerge as a result of the internal structure of the two classes of deposits.
Siliciclastic sediments, such as sandstones and shale, develop through the attrition of other rocks. Their grains are sorted prior to deposition. Sandstones and shale are formed of sedimentary particles derived from sources outside the depositional basin. Siliciclastic sediments are relatively stable after deposition. As a result, the pore space in sandstones is mainly intergranular and its complexity depends on the degree of sorting.
In contrast, carbonates form in special environments and are biochemical in nature. They are essentially autochthonous, as they form very close to the final depositional sites. They are not transported and sorted in the same way as sandstones. Carbonates are usually deposited very close to their source and develop as a result of various processes. Their texture is more dependent on the nature of the skeletal grains than on external influences. Intrabasinal factors control facies development. Reefs, bioherms, and biostroms are examples of in-place local deposition where organisms have built wave-resistant structures above the level of adjacent time-equivalent sediments.
Carbonates are characterized by different types of porosity and have unimodal, bimodal, and other complex pore structure distributions. These distributions result in wide permeability variations for the same total porosity, making it difficult to predict for example the production efficiency for hydrocarbon. Carbonate rock texture produces spatial variations in permeability and capillary bound water volumes.
Carbonates are particularly sensitive to post-depositional diagenesis including dissolution, cementation, recrystallization, dolomitization, and replacement by other minerals. Calcite can be readily dolomitized, sometimes increasing porosity. Complete leaching of grains by meteoric pore fluids can lead to textural inversion which may enhance reservoir quality through dissolution or occlude reservoir quality through cementation. Burial compaction fracturing and stylolithification are common diagenetic effects in carbonates, creating high-permeability zones and permeability barriers or baffles, respectively. Diagenesis can cause dramatic changes in carbonate pore size and shape. On a large scale, porosity due to fracturing or dissolution of carbonate rocks can produce “pores” up to the size of caverns.
All carbonate sediments are composed of three textural elements which are defined as grains, matrix, and cement, respectively. In general, geologists have attempted to classify sedimentary rocks on a natural basis, but some schemes have genetic implications, i.e., knowledge or origin of a particular rock type is assumed.
The relative proportions of the components, among others, can be used to classify carbonate sediments. A widely used classification scheme is proposed by Dunham (see Dunham, “Classification of carbonate rocks according to depositional texture”, in Classification of carbonate rocks—A Symposium, Ham, ed., volume 1, pages 108-121. AAPG Mem., 1962.) In Dunham, carbonates are classified based on the presence or absence of lime mud and grain support. Textures range from grainstone, rudstone, and packstone (grain-supported) to wackestone and mudstone (mud-supported). Where depositional texture is not recognizable, carbonates are classified as boundstone or crystalline. Within these carbonates, the porosity takes many forms, depending on the inherent fabric of the rock, and on the types of processes that can occur during and after deposition.
Another classification system, by Lucia (see Lucia, Petrophysical parameters estimated from visual description of carbonate rocks: a field classification of pore space. Journal of Petroleum Technology, 35:626-637, March 1983) is based on petrographical attributes and porosity. Dolomites are included in this classification scheme.
Pore type characterization is used in a classification scheme of Choquette and Pray (see P. W. Choquette and L. C. Pray. Geologic nomenclature and classification of porosity in sedimentary carbonates. PAPG Bull., 54:207-250, 1970). Choquette and Pray, in contrast to Dunham, classify carbonates according to fabric and nonfabric pore types. Examples of the former are inter- and intraparticle porosity, while those of the latter are fractures and vugs. Other classification schemes differentiate between primary and secondary pore spaces using the description based on classification according to Choquette and Pray.
Methods are known in which some of the petrographical information obtained using these classifications is used to improve the petrophysical evaluation of the geological formations.
Interpretation of well logs for use in subsurface geology is long-established and remains fundamental to the construction of accurate reservoir models. Well logs are used to detect the range and characteristics of rock types that exist within a reservoir, and seismic data together with geological knowledge are used to propagate this information into inter-well space. Well log data is also used to aid the development of depositional and sequence stratigraphic models, as well as to assess the distribution of petrophysical properties within a reservoir.
For example in SPE 26498, presented at the 68th SPE Annual Technical Conference and Exhibition , Houston, Tex., USA in Oct. 3-6, 1993, a method is presented that uses density, neutron porosity, sonic travel time, gamma ray and water saturation as input to a processing step. The processing uses correlation techniques to classify carbonates in the absence of core data.
Well log data is also used to aid the development of depositional and sequence stratigraphic models, as well as to assess the distribution of petrophysical properties within a reservoir.
Many of these techniques require accurate identification of both depositional facies and diagenetic overprints, and the placing of these within a stratigraphic model that offers a degree of predictability in regions of the reservoir with little or sparse data. This exercise necessarily requires the initial erection of valid criteria for well-to-well correlation, based on either lithostratigraphy (correlation based on depositional lithology) that will yield a simple stratigraphic model, or chronostratigraphy (correlation based on division of the stratigraphy into units of time bounded by coeval timelines). In turn, these improvement will result in a more sophisticated sequence stratigraphic model. The formulation of such models is vital in that they provide a framework for predictions of the hydrocarbon distribution, volume in-place, the geometry and continuity of flow units within the model, and the formulation of recovery strategies.
In a reservoir assessment, it is common to include data from more than one well. Most cross-well correlation methods utilize gamma, density, porosity, and resistivity logs. In carbonate fields, current correlation techniques often rely heavily upon gamma ray signatures, which are usually inferred to mark clay-rich horizons. In carbonate successions, these are often, but not exclusively, found either at the base of depositional sequences or near maximum flooding surfaces. Density logs mark changes in porosity and so can detect, for example, alternations of zones of reservoir quality and denser zones in stacked successions.
There exists a desire to improve the interpretation of well data, particularly for carbonate-type reservoirs.